interpretDetect ambiguity in user messages using the RPCS-1 Signature Ambiguity Framework. Returns AR level (AR0-AR5), confidence, candidate interpretations with scores, clarifying questions, and suggested next step. Use when a user says something vague, passive-aggressive, or underspecified.
| Name | Required | Description | Default |
|---|---|---|---|
| risk | No | Risk category for ambiguity threshold. | advice |
| text | Yes | The message to interpret. |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral detail about outputs (AR level, confidence, candidate interpretations, clarifying questions, suggested next step), which goes beyond the annotations and helps set expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a first sentence states the core purpose and framework, a second lists key outputs, and a final clause gives a concrete trigger condition. No wasted words, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description appropriately enumerates the main return fields (AR level, confidence, candidates, questions, next step). It also gives usage context. It could optionally clarify how the 'risk' parameter affects thresholding, but the schema already explains the parameter options, leaving the description sufficient for basic selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'text' and 'risk' parameters fully described in the schema. The description does not add significant parameter-level semantics beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool's verb ('Detect ambiguity') and resource ('user messages'), and names the specific framework (RPCS-1 Signature Ambiguity Framework). It differentiates from siblings like 'rewrite' or 'normalize' by focusing on ambiguity detection and producing interpretation artifacts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use ('Use when a user says something vague, passive-aggressive, or underspecified'). It does not mention when not to use it or explicitly name alternatives, but the use context is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes: interpret, prepare_prompt, and route_intent all deal with ambiguity detection and clarification decisions. Their boundaries are subtle (e.g., prepare_prompt returns a commit-vs-clarify decision but defers to route_intent as authority), which will likely confuse an agent trying to select the right tool. The descriptions try to differentiate them, but the functional overlap is significant.
The tool names mix conventions: single verbs (interpret, normalize, rewrite) and verb_noun compounds (calibrate_profile, prepare_prompt, render_reply, route_intent). While the verb_noun names are consistent among themselves, the single-verb names break the pattern, giving an overall mixed but readable set.
8 tools is a well-scoped count for the server's purpose (agent tuning and translation bridge). Each tool serves a distinct function in the pipeline without being excessive, and the number is within the ideal 3-15 range.
The core lifecycle (calibrate -> prepare -> route -> render) is well-covered, and auxiliary tools (interpret, normalize, rewrite, recommend) fill common side needs. Minor gaps exist, such as no explicit profile update tool and redundancy between prepare_prompt and route_intent, but agents can work around these.